This course aims to provide an introduction to the design and use of deep learning models and reinforcement learning approaches for sensor data processing, machine vision and robotics.  The first part of the course introduces the basic concepts and fundamentals of machine learning and neural networks. The second part presents advanced deep models and their use in monitoring, understanding, control and planning tasks, with focus on robotics and distributed sensing application scenarios. Presentation of the theoretical models and associated algorithms will be complemented by references to popular software frameworks and code. Given the course focus, much of the concepts and models presented will deal with sequential data (e.g. sensor or control timeseries) and visual data (images and video), with insights on relevant problems, including lifelong learning, reinforcement learning, federated learning and learning under resource constraints.